Digital Phenotyping – Turning our Smartphones Inward

Digital Phenotyping – Turning our Smartphones Inward

Publication date: Apr 12, 2019

– James Comey, Former FBI DirectorTo put digital phenotyping in perspective, let’s dive into the interplay between genetics, phenotypes and the digital world.

Our phenotype - our observable traits, including our eye colour, height, gender characteristics, biochemical and physiological composition and behaviour - is a product of our genetics, our environment, the interplay between the two (GxE interactions), and the social influence to which we’re all exposed.

Think of an online dating profile, a social media post, an online grocery order or a Medium article (on digital phenotyping?)

As we use our smartphones to continuously evolve our extended online phenotypes and interact with the digital world, we leave behind digital fingerprints.

These are picked up by our smartphone’s (and wearables’) sensors to create our digital phenotype.

GPS location and movement, battery recharge frequency, voice patterns and speech, length or frequency of texts/calls, number of times visiting a certain app, the way our smartphone is organised, the angle at which we often hold our smartphone or even the frequency of app updates - all these data can make up our digital phenotype.

Together with traditional clinical approaches to characterising diseases (e. g., blood tests, imaging, physical exam), digital phenotypes can help shape an understanding of our overall health, illness and real-world behaviour.

Our smartphone usage and the digital fingerprints we leave behind create an extended digital phenotype that provides insights into our health and behaviour.

At the heart of this new smartphone usage evolution is digital phenotyping and the insights we can glean from this space.

Imagine if you could: Uncover diseases you might have but are not aware of List diseases that you might be susceptible to without any blood/genetic tests Predict how responsive (or not) you might be to certain medications Understand how likely you are to adhere to certain medication Predict how your disease will progress over time No longer do we need to imagine, as while it’s still early days for digital phenotyping, here are some of the conditions that can already be detected: We’re starting to turn our smart devices inward and use them as a tool to learn about ourselves.

Here’s what they did: Knowing that there’s a strong relationship between physical activity and depression, they used the smartphone’s acceleration data to analyse activity and walking time GPS information was used every 15 minutes to get the subjects’ location to calculate the distance they had traveled WiFi-based location logging was used to measure the time a subject stayed at home Total phone usage time was analysed, as were the number of incoming and outgoing calls and text messages, and the total number of contacts these communications were with as a benchmark of social activity Number of Calendar events were counted as a proxy for stress By the end of the study, combining these digital phenotypes led to predicting depression with ~60% accuracy.

Moving away from mental health, in another study, researchers wanted to see whether digital phenotyping could be used to assess how likely older people are to suffer from falls.

And what about using digital phenotyping together with our extended online phenotype to predict our genetic encoding?

This 117-subject study showed that people with the -pro-social” genetic variant of the oxytocin receptor had a significantly larger -social network”, as measured by the total number of names and phone numbers saved in their smartphone Contacts, and number of incoming calls.

Concepts Keywords
Acceleration Larger social network
Accelerometer Diseases healthcare systems
Algorithm Healthcare trend
Autistic Turn smart devices
Battery Profile social media
Biochemical Healthcare players
Blood PDA
Blood Pressure Mobile phones
Cardiovascular GPS
Chemistry Smartphones
CIO Gene–environment interaction
Clinical Trials Phenotype
Creditworthiness Digital phenotyping
Cybersecurity Bioinformatics
Depression Classical genetics
Digital Academic disciplines
Earthworms Branches of biology
Empathy Contacts communications benchmark
Evolution Turn devices
Exponential Potential healthcare words
FBI Transportation assets skyscrapers
Frequency
Fresh Air
Gait
Gender
Genetic
Genetic Variant
Genetics
GPS
Gyroscope
Healthcare
Hormone
Imagine
Insurance
Interact
Interplay
IPhone
James Comey
Jogging
John McAfee
Logging
Mail
Melanomas
Mental Health
Oxytocin
Parkinsonian
PDA
Pharma
Phenotype
Phenotypes
Physical Activity
Physical Exam
Planet
Population Health
Pregnancy
Privacy
Pun
Receptors
Richard Dawkins
Sandboxed
Sequencing
Sleep
Smartphone
Smartphones
Social Influence
Social Media
Social Network
Stress
Sudoku
Swedish
Transit
Triaging
Trillion
Uber
Voltaire
Wearables
Yesterdays

Semantics

Type Source Name
drug DRUGBANK Tropicamide
gene UNIPROT WASHC1
gene UNIPROT EHD1
drug DRUGBANK Medical air
gene UNIPROT CTSB
disease MESH melanomas
disease MESH tremors
gene UNIPROT SET
gene UNIPROT EAF2
gene UNIPROT IMPACT
gene UNIPROT SPINK5
gene UNIPROT SLC35G1
disease DOID GPS
gene UNIPROT NBEAL2
gene UNIPROT APP
gene UNIPROT LITAF
disease MESH visual
drug DRUGBANK Nonoxynol-9
disease MESH Depression
gene UNIPROT SMIM10L2B
gene UNIPROT SMIM10L2A
gene UNIPROT THOP1
disease MESH gait
gene UNIPROT REG3A
gene UNIPROT HHIP
gene UNIPROT RPL29
gene UNIPROT ST13
gene UNIPROT KCNK3
gene UNIPROT PTPN5
drug DRUGBANK Oxytocin
pathway BSID Reproduction
gene UNIPROT RXFP2
gene UNIPROT PLEKHG5
disease MESH habits
gene UNIPROT NFKBIZ
disease MESH emergency
disease MESH lifestyles
disease MESH cardiovascular disease
disease MESH multiple
disease MESH medication adherence
gene UNIPROT FASTK
gene UNIPROT TNFSF13B

Original Article

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